Estimating neighborhood-based mortality risk associated with air pollution: A prospective study.

Effect modification of integrated neighborhood environment on associations of air pollution with mortality remained unclear. We analyzed data from UK biobank prospective study (n = 421,650, median 12.5 years follow-up) to examine disparities of mortality risk associated with air pollution among varied neighborhood settings. Fine particulate matter (PM2.5), PM10 and nitrogen dioxide (NO2) were measured and assigned to each participants' address. Diverse ecological and societal settings of neighborhoods were integrated with principal component analysis and categorized into disadvantaged, intermediate and advantaged levels. We estimated mortality risk associated with air pollution across diverse neighborhoods using Cox regression. We calculated community-level proportions of mortality attributable to air pollutants. There was evidence of higher all-cause and respiratory disease mortality risk associated with PM2.5 and NO2 among those in disadvantaged neighborhoods. In disadvantaged communities, air pollutants explained larger proportions of deaths and such disparities persisted over past decades. Across 2010-2021, reducing PM2.5 and NO2 to 10 µg/m3 (World Health Organization limits) would save 87,000 (52,000-120,000) and 91,000 (37,000-145,000) deaths of populations aged ≥ 40 years, with 150 000 deaths occurred in disadvantaged neighborhood settings. These findings suggested that disadvantaged neighborhoods can exacerbate mortality risk associated with air pollution.

Spatiotemporal variations of "triple-demic" outbreaks of respiratory infections in the United States in the post-COVID-19 era.

BACKGROUND: The US confronted a "triple-demic" of influenza, respiratory syncytial virus (RSV), and COVID-19 in the winter of 2022, leading to increased respiratory infections and a higher demand for medical supplies. It is urgent to analyze these epidemics and their spatial-temporal co-occurrence, identifying hotspots and informing public health strategies. METHODS: We employed retrospective and prospective space-time scan statistics to assess the situations of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and from October 2022 to February 2023, respectively. This enabled monitoring of spatiotemporal variations for each epidemic individually and collectively. RESULTS: Compared to winter 2021, COVID-19 cases decreased while influenza and RSV infections significantly increased in winter 2022. We found a high-risk cluster of influenza and COVID-19 (not all three) in winter 2021. In late November 2022, a large high-risk cluster of triple-demic emerged in the central US. The number of states at high risk for multiple epidemics increased from 15 in October 2022 to 21 in January 2023. CONCLUSIONS: Our study offers a novel spatiotemporal approach that combines both univariate and multivariate surveillance, as well as retrospective and prospective analyses. This approach offers a more comprehensive and timely understanding of how the co-occurrence of COVID-19, influenza, and RSV impacts various regions within the United States. Our findings assist in tailor-made strategies to mitigate the effects of these respiratory infections.

Spatiotemporal Variations of "Triple-demic" Outbreaks of Respiratory Infections in the United States in the Post-COVID-19 Era.

Objectives: The United States confronted a "triple-demic" of influenza, respiratory syncytial virus, and COVID-19 in the winter of 2022, resulting in increased respiratory infections and a higher demand for medical supplies. It is urgent to analyze each epidemic and their co-occurrence in space and time to identify hotspots and provide insights for public health strategy. Methods: We used retrospective space-time scan statistics to retrospect the situation of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and then applied prospective space-time scan statistics to monitor spatiotemporal variations of each individual epidemic, respectively and collectively from October 2022 to February 2023. Results: Our analysis indicated that compared to the winter of 2021, COVID-19 cases decreased while influenza and RSV infections increased significantly during the winter of 2022. We revealed that a twin-demic high-risk cluster of influenza and COVID-19 but no triple-demic clusters emerged during the winter of 2021. We further identified a large high-risk cluster of triple-demic in the central US from late November, with COVID-19, influenza, and RSV having relative risks of 1.14, 1.90, and 1.59, respectively. The number of states at high risk for multiple-demic increased from 15 in October 2022 to 21 in January 2023. Conclusion: Our study provides a novel spatiotemporal perspective to explore and monitor the transmission patterns of the triple epidemic, which could inform public health authorities' resource allocation to mitigate future outbreaks.

Cross-regional analysis of the association between human mobility and COVID-19 infection in Southeast Asia during the transitional period of "living with COVID-19".

BACKGROUND: In response to COVID-19, Southeast Asian (SEA) countries had imposed stringent lockdowns and restrictions to mitigate the pandemic ever since 2019. Because of a gradually boosting vaccination rate along with a strong demand for economic recovery, many governments have shifted the intervention strategy from restrictions to "Living with COVID-19" where people gradually resumed their normal activities since the second half of the year 2021. Noticeably, timelines for enacting the loosened strategy varied across Southeast Asian countries, which resulted in different patterns of human mobility across space and time. This thus presents an opportunity to study the relationship between mobility and the number of infection cases across regions, which could provide support for ongoing interventions in terms of effectiveness. OBJECTIVE: This study aimed to investigate the association between human mobility and COVID-19 infections across space and time during the transition period of shifting strategies from restrictions to normal living in Southeast Asia. Our research results have significant implications for evidence-based policymaking at the present of the COVID-19 pandemic and other public health issues. METHODS: We aggregated weekly average human mobility data derived from the Facebook origin and destination Movement dataset. and weekly average new cases of COVID-19 at the district level from 01-Jun-2021 to 26-Dec-2021 (a total of 30 weeks). We mapped the spatiotemporal dynamics of human mobility and COVID-19 cases across countries in SEA. We further adopted the Geographically and Temporally Weighted Regression model to identify the spatiotemporal variations of the association between human mobility and COVID-19 infections over 30 weeks. Our model also controls for socioeconomic status, vaccination, and stringency of intervention to better identify the impact of human mobility on COVID-19 spread. RESULTS: The percentage of districts that presented a statistically significant association between human mobility and COVID-19 infections generally decreased from 96.15% in week 1 to 90.38% in week 30, indicating a gradual disconnection between human mobility and COVID-19 spread. Over the study period, the average coefficients in 7 SEA countries increased, decreased, and finally kept stable. The association between human mobility and COVID-19 spread also presents spatial heterogeneity where higher coefficients were mainly concentrated in districts of Indonesia from week 1 to week 10 (ranging from 0.336 to 0.826), while lower coefficients were mainly located in districts of Vietnam (ranging from 0.044 to 0.130). From week 10 to week 25, higher coefficients were mainly observed in Singapore, Malaysia, Brunei, north Indonesia, and several districts of the Philippines. Despite the association showing a general weakening trend over time, significant positive coefficients were observed in Singapore, Malaysia, western Indonesia, and the Philippines, with the relatively highest coefficients observed in the Philippines in week 30 (ranging from 0.101 to 0.139). CONCLUSIONS: The loosening interventions in response to COVID-19 in SEA countries during the second half of 2021 led to diverse changes in human mobility over time, which may result in the COVID-19 infection dynamics. This study investigated the association between mobility and infections at the regional level during the special transitional period. Our study has important implications for public policy interventions, especially at the later stage of a public health crisis.

Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method.

BACKGROUND: The COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA. OBJECTIVE: The purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19. METHODS: We collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages. RESULTS: We found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between -0.05 and -1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic. CONCLUSIONS: The analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19.

Analyzing the farmers' pro-environmental behavior intention and their rural tourism livelihood in tourist village where its ecological environment is polluted.

This study examines farmers' intentions towards pro-environmental behavior in a famous tourist village in China called Guanshan, whose ecological environment is polluted. By adopting the empirically validated norm activation model (NAM) of Schwartz and merging it with Vroom's expectancy theory, the current research aims to develop a refined framework for understanding the formation of and predicting changes in pro-environmental intention. Field surveys were conducted in Guanshan, which resulted in sample data consisting of 275 valid responses collected by the research team. We develop a refined model, including six latent variables and 24 observational items. The structural equation modeling results show that the final model enjoys a better predictive accuracy of pro-environmental intention than does the original NAM. The study also discovers that the motivational force of expectancy theory significantly influences pro-environmental intention, whose motivational force comes from the impact of valence and expectancy. The pro-environmental intentions of farmers are mainly affected by the environmental personal norm and by a certain degree of personal expectancy. The improvement of farmers' pro-environmental intention needs be promoted in two approaches: the cultivation of personal environmental protection norms and the guidance of producing a desired expectation for pro-environmental intention.